// Copyright (c) 2023 Huawei Technologies Co., Ltd
// All rights reserved.
//
// Licensed under the BSD 3-Clause License  (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#include "op_plugin/AclOpsInterface.h"
#include "op_plugin/OpApiInterface.h"
#include "op_plugin/utils/op_api_common.h"

namespace op_api {
using npu_preparation = at_npu::native::OpPreparation;

at::Tensor l1_loss(const at::Tensor& self, const at::Tensor& target, int64_t reduction)
{
    DO_COMPATIBILITY(aclnnL1Loss, acl_op::l1_loss(self, target, reduction));
    // construct the output tensor of NPU
    // 1. If reduction='none', the output size should be the same size as self.
    // 2. Otherwise pass {} to apply_tensor.
    // 3. Dtype of output should be the same dtype as self.
    at::IntArrayRef output_size;
    if (reduction == at::Reduction::None) {
        auto output_size_vec = op_infer::broadcast_ops_npu_output_size(self, target);
        output_size = output_size_vec;
    }
    auto promote = at::native::result_type(target, self);
    at::Tensor result = npu_preparation::apply_tensor_without_format(output_size, self.options().dtype(promote));
    // dispatch hostAPI
    EXEC_NPU_CMD(aclnnL1Loss, self, target, reduction, result);
    return result;
}

} // namespace op_api
